dermen / simemc

diffraction simulations and EMC

simemc

diffraction simulations and EMC

Dependencies

• Developer build of CCTBX with mpi4py support
• reborn
• sympy
• pytest-mpi (optional)
• kern line profiler
• CUB (for doing block reduction in CUDA, see the example build_mpi_trilerp.sh script)
• Possibly some other python dependencies, run the tests and install any missing dependencies using PIP

Building

1. Clone simemc (if not using ssh keys for github, change URL)

git clone [email protected]:dermen/simemc.git

2. Install dev CCTBX (edit and run the script build_cctbx_dev_gpu_mpi.sh) . Note what you set as the value of CCTBXROOT. This might take some tinkering with (DIALS/CCTBX mailining lists are active and responsive)

cd simemc
./build_cctbx_dev_gpu_mpi.sh

3. Activate the cctbx environment

source CCTBXROOT/build/conda_setpaths.sh
# Note, to deactivate run CCTBXROOT/build/conda_unsetpaths.sh

4. Install reborn. Edit the value of CCTBXROOT in build_reborn.sh and run the script

./build_reborn.sh

5. Get the other python dependencies

libtbx.pip install pytest-mpi line_profiler sympy

6. Build the simemc extension module from the root of the simemc repository (copy the build_mpi_trilerp.sh example script, and edit it accordingly) . Note, the build_trilerp* scripts are deprecated and should not be used. See build_mpi_trilerp_pm.sh for an example build script to use on perlmutter.

cd simemc
./build_mpi_trilperp.sh

7. Softlink simemc to the cctbx modules folder

ln -s /full/path/to/simemc $CCTBXROOT/modules

8. Make a startup script that can be sourced at each new login. Example:

#!/bin/bash
export PATH=/usr/local/cuda/bin:$PATH
export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=/usr/local/cuda/lib64
export CCTBXROOT=~/xtal_gpu  # whatever this was set as when CCTBX was built
source $CCTBXROOT/build/conda_setpaths.sh

Testing

From the repository root run libtbx.pytest. Then, optionally test the mpi4py installation using e.g. mpirun -n 2 libtbx.pytest --with-mpi --no-summary -q (provided pytest-mpi is installed).

The first time you run the tests, first run iotbx.fetch_pdb 4bs7. That command will download the PDB file used in the simulations.

Run the pipeline

Here we simulate 999 shots on a machine with 24 processors and 1 v100 GPU. We need to first downloaded the PDB file 4bs7 using iotbx.fetch_pdb 4bs7. We then create a quaternion file containing the orientation samples.

# prep (from the simemc repository root):
iotbx.fetch_pdb 4bs7
cd quat
gcc make-quaternion.c -O3 -lm -o quat
./quat -bin 70
cd ../

The script then runs them through EMC for a set number of iterations

DIFFBRAGG_USE_CUDA=1 mpirun -n 3 libtbx.python tests/emc_iteration.py  1 333 water_sims --niter 100 --phil proc.phil  --minpred 3 --hcut 0.1  --xtalsize 0.0025 --densityUpdater lbfgs

Process the images using the standard stills process framework as a comparison:

mpirun  -n 24 dials.stills_process \
  proc.phil  water_sims/cbfs/*.cbf filter.min_spot_size=2 \
  output.output_dir=water_sims/proc mp.method=mpi

mpirun -n 24 cctbx.xfel.merge merge.phil \
  input.path=water_sims/proc output.output_dir=water_sims/merge

Because we ran a simulation, we know the ground truth structure factors. We can plot the correlation between the EMC-determined structure factors with the ground truth. We can do the same for stills_process / xfel.merge determined structure factors. See the script make_corr_plot.py:

libtbx.python make_corr_plot.py water_sims/Witer10.h5  --mtz water_sims/merge/iobs_all.mtz

Note the EMC-determined structure factors correlate much better for these simulated data with low spot counts.